CN113919515A - Preventive maintenance method and system for dry etching machine and storage medium - Google Patents

Preventive maintenance method and system for dry etching machine and storage medium Download PDF

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CN113919515A
CN113919515A CN202010656426.8A CN202010656426A CN113919515A CN 113919515 A CN113919515 A CN 113919515A CN 202010656426 A CN202010656426 A CN 202010656426A CN 113919515 A CN113919515 A CN 113919515A
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dry etching
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杨冬野
袁蕾
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Suzhou Xinhuilian Semiconductor Technology Co ltd
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Abstract

The invention discloses a preventive maintenance method, a preventive maintenance system and a storage medium for a dry etching machine, wherein the method comprises the following steps: analyzing the defect mechanism of the defective dry etching test product by means of characterization of material structure and performance to obtain defect types and major and minor factors of generating equipment and process; according to the analysis result, a defect prediction model is established by adopting a statistical method; establishing a maintenance threshold value for normal operation of the dry etching machine according to the defect prediction model; and pushing a normal/alarm response according to the comparison between the maintenance threshold and the acquired characteristic parameters of the current dry etching machine, and matching corresponding maintenance contents and plans. The invention realizes the effective improvement of the operation stability of the equipment and the yield of products.

Description

Preventive maintenance method and system for dry etching machine and storage medium
Technical Field
The invention belongs to the technical field of equipment debugging, and particularly relates to a preventive maintenance method and system for a dry etching machine and a storage medium.
Background
With the development of semiconductor chip etching preparation technology, dry etching has mostly replaced wet etching to become the main method of patterned etching. The requirements on the working conditions of equipment in the dry etching process are higher and higher, and the requirements comprise transmission mechanism precision, gas flow and flow field stability, electrostatic chuck adsorption force uniformity, reaction cavity gas, etching residues and the like. If working conditions occur in the etching process, the qualification rate of online products is reduced, and huge economic loss is caused. Therefore, all functions of the equipment need to be preventively maintained before the equipment works, the running stability of the equipment is improved, and the yield of products is improved.
However, most of the maintenance and repair of the existing dry etching equipment are in a passive maintenance mode, that is, the maintenance is performed only when the equipment fails, and the main problems include: 1. the fault treatment of the dry etching machine is temporary and permanent, and the source is unknown; 2. the dry etching equipment is maintained by manufacturers, and the preventive maintenance effect is not obvious.
Disclosure of Invention
The invention aims to solve the technical problems and provides a preventive maintenance method, a preventive maintenance system and a storage medium for a dry etching machine, so that the operation stability of equipment and the yield of products can be effectively improved.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the preventive maintenance method of the dry etching machine comprises the following steps:
analyzing the defect mechanism of the defective dry etching test product by means of characterization of material structure and performance to obtain defect types and major and minor factors of generating equipment and process;
according to the analysis result, a defect prediction model is established by adopting a statistical method;
establishing a maintenance threshold value for normal operation of the dry etching machine according to the defect prediction model;
and pushing a normal/alarm response according to the comparison between the maintenance threshold and the acquired characteristic parameters of the current dry etching machine, and matching corresponding maintenance contents and plans.
Specifically, the characterization means comprises scanning electron microscope characterization, transmission electron microscope characterization and X-ray diffraction.
Specifically, the defect prediction model is obtained by learning according to data obtained after the tissue and performance characterization of the dry etching test product and the defect occurrence time of the test product or the equipment maintenance time; or the defect prediction model is obtained by learning according to the dynamic coefficient of the equipment when the dry etching equipment is detected, and the time when the equipment fails or the time when maintenance is carried out.
Specifically, before the on-line detection device of the dry etching equipment is used for obtaining the equipment characteristic parameters of the dry etching machine in the current production process, the method further comprises the following steps: determining the type and state of a product, and acquiring current temperature and humidity data;
and determining the current equipment parameters corresponding to the dry etching machine according to the product type and state, the current temperature and humidity data.
Specifically, after predicting when a defect will occur in the current dry etching product according to the defect prediction model, the method further includes: and determining a maintenance scheme of the dry etching machine according to a result of predicting when the current dry etching product has the defects.
The preventive maintenance system of the dry etching machine comprises
The maintenance threshold value acquisition module is used for acquiring a maintenance threshold value of the dry etching machine;
the dynamic data acquisition module is used for acquiring numerical values corresponding to a plurality of characteristic parameters of the dry etching machine equipment;
and the preventive maintenance suggestion acquisition and pushing module is interconnected with the maintenance threshold acquisition module and the dynamic data acquisition module and is used for acquiring a plurality of comprehensive preventive values according to the values corresponding to the characteristic parameters and the maintenance threshold and pushing the comprehensive preventive maintenance suggestions and/or the alarm processing suggestions which are matched with the comprehensive preventive maintenance suggestions.
Specifically, the preventive maintenance advice acquisition and pushing module may determine whether the characteristic parameter exceeds a maintenance threshold and the duration exceeds a preset time, and if the determination result is yes, push the matched preventive maintenance advice and/or alarm processing advice.
A dry etcher preventive maintenance storage medium storing a computer-executable program which, when executed by a computer processor, implements the dry etcher preventive maintenance method as recited in any one of claims 1 to 5.
Compared with the prior art, the preventive maintenance method, the preventive maintenance system and the storage medium of the dry etching machine have the advantages that:
the preventive maintenance method, the preventive maintenance system and the storage medium for the dry etching machine, provided by the invention, can prompt maintenance personnel to carry out targeted preventive maintenance on dry etching equipment in time, accurately position a product defect source, change passive maintenance into active protection, and improve the operation stability and the product yield of the equipment.
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Fig. 1 is a flowchart of implementation of a method and a system for preventive maintenance of a dry etching machine according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are described clearly and completely below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
Example 1:
the embodiment can be suitable for the situation of preventive detection of the dry etching machine, and the method can be integrated in a detection system of the dry etching machine.
As shown in fig. 1, the preventive maintenance method for a dry etcher includes:
the method is characterized in that a defect test and analysis is performed on a test product with defects, wherein the defect test and analysis comprises Scanning Electron Microscope characterization (hereinafter abbreviated as SEM), Transmission Electron Microscope characterization (hereinafter abbreviated as TEM), and X-ray Diffraction (hereinafter abbreviated as XRD), and the characterization device is not specifically limited, and can be a material characterization device purchased from a factory for testing in a production interval, or can be entrusted to colleges or testing institutions for defect detection and analysis.
When the equipment is abnormal, for example, when helium leaks and goes down too much, particles such as polymer on the surface of the ESC can be blown to the surface of wafer to block the BARC etching, so that residues are formed in the final etching Poly, and a grid shape which is not needed in actual production is formed, and the final product is abnormal. And then selecting abnormal products to perform performance test and material structure characterization, determining the distribution positions and microstructure images of the defects in the products, calculating the defect occurrence probability, and analyzing the defect occurrence reasons. And (3) adopting the SEM to perform retest observation on the defect points obtained by scanning the defect detection machine, if the appearance of the crystal defects is flat or the crystal defects show a structure similar to a flower shape, then respectively performing element analysis on the crystal defects and the peripheral substrate by using an element analysis function integrated in the SEM, finding out a main signal element in the substrate as a background, and simultaneously finding out an oxygen element signal from the surface of the oxide, wherein if fluorine elements with higher signals and a small amount of oxygen element signals exist in the crystal defects besides the main signal element, the crystal defects are a crystalline compound consisting of aluminum, fluorine and oxygen. Since fluorine is present in a high proportion in the defect, it is considered as one of the main causes of defect generation, and therefore, in analyzing the cause of defect generation, the process step of introducing fluorine on the surface of the product should be analyzed with emphasis.
The defect prediction model is obtained by learning according to the organization and performance characterization post-analysis of the dry etching test product and the defect occurrence time of the test product or the equipment maintenance time;
or the defect prediction model is obtained by learning according to the dynamic coefficient of the equipment when the dry etching equipment is detected, and the time when the equipment fails or the time when maintenance is carried out. The model training is based on the corresponding relation between the defect data of the conventional dry etching test product and the actual failure time or the actual maintenance time of the equipment. Therefore, the time that the equipment is likely to have product defects or needs to be maintained can be calculated according to the defect data of the current dry etching product.
For example, two wafers are found to be scrapped in a batch of products, the preliminary measure is to clean the interlocking vacuum chamber with an alarm, the defective products are further analyzed, and the fundamental reason for the defect is to find out that the interlocking vacuum chamber gives an alarm, and the polymer deposited by the ESC falls off when the whole machine is idle for 10 min. In another example, two wafers Dome Defect are found in a reaction chamber 1 in a batch of products, the primary measure is to clean the chamber where Defect occurs, and further analyze the defective products to find out that the root cause of Defect generation is damage of a regulator of compressed air, and the flow of compressed air is unstable in the normal running process, so that deposited polymer falls off.
In this embodiment, the defect data may be a dynamic coefficient of a current device or data that is not converted, and the defect prediction model may be integrated in an intelligent terminal, such as a mobile phone, a computer, or the like, and may also be stored in a server. Further, a database can be established through the defect data at the same time, and a plurality of maintenance thresholds of each part of the dry etching equipment are formed. The database establishing step can be executed before the dynamic data acquisition step, and the device characteristic parameters needing to be acquired by the dry etching device and the maintenance threshold corresponding to the characteristic parameters are preset, and then the numerical value acquisition of the characteristic parameters of the next step is carried out.
Before the on-line detection device of the dry etching equipment is used for obtaining the characteristic parameters of the equipment in the current production process of the dry etching machine, the method also comprises the following steps:
determining the type and the state of the product, and acquiring current ambient temperature and humidity data. And determining the current equipment parameters corresponding to the dry etching machine according to the product type and state, the current temperature and humidity data. The method can directly acquire the relevant data of the online detection device of the dry etching equipment to form characteristic parameters.
The online detection device is arranged on a sensor, an actuator, a power metering device and the like of the dry etching equipment to collect the operation data of the equipment in real time, such as an intelligent electric energy meter, a frequency converter, a temperature sensor, a humidity sensor, a pressure and differential pressure sensor, an intelligent control module and the like.
The characteristic parameter database of the dry etching equipment is different at different stages in the whole life cycle of the dry etching equipment, such as voltage, power supply frequency, response time, power consumption, water flow, vacuum condition, equipment temperature, pressure, load rate, unbalance degree and the like.
Predicting when the current dry etching product will have defects according to the defect prediction model, and the method further comprises:
and determining a maintenance scheme of the dry etching machine according to a result of predicting when the current dry etching product has the defects.
Obtaining a maintenance threshold value and a current characteristic parameter of the dry etching equipment according to the steps, adopting a correlation analysis method, pushing a matched preventive maintenance suggestion and/or an alarm processing suggestion and the like, wherein the specific content and the plan of the maintenance scheme of the dry etching equipment comprise whether maintenance is needed within a period of time, how long maintenance is expected to be needed, and a specific maintenance mode, such as component replacement or parameter adjustment and the like.
Example 2:
the embodiment is a preventive maintenance system of a dry etching machine, comprising:
the maintenance threshold value acquisition module is used for acquiring a maintenance threshold value of the dry etching machine; further, the defect prediction model is obtained by learning according to the analysis after the dry etching test product organization and performance characterization and the time when the test product has defects or the time when equipment maintenance is carried out; or the defect prediction model is obtained by learning according to the dynamic coefficient of the equipment when the dry etching equipment is detected, and the time when the equipment fails or the time when maintenance is carried out.
The model training is based on the corresponding relation between the defect data of the conventional dry etching test product and the actual failure time or the actual maintenance time of the equipment. The time that the equipment may have product defects or need to be maintained can be calculated according to the defect data of the current dry etching product. The defect data may be dynamic coefficients of current devices or data that is not converted, and the defect prediction model may be integrated in an intelligent terminal, such as a mobile phone, a computer, or the like, and may also be stored in a server.
Further, a database can be established through the defect data at the same time, and a plurality of maintenance thresholds of each part of the dry etching equipment are formed. The database establishing step can be executed before the dynamic data acquisition step, and the device characteristic parameters needing to be acquired by the dry etching device and the maintenance threshold corresponding to the characteristic parameters are preset, and then the numerical value acquisition of the characteristic parameters of the next step is carried out.
The dynamic data acquisition module is used for acquiring numerical values corresponding to a plurality of characteristic parameters of the dry etching machine equipment; furthermore, the online detection device is installed on a sensor, an actuator, a power metering device and the like of the dry etching equipment to collect operation data of the equipment in real time, such as an intelligent electric energy meter, a frequency converter, a temperature sensor, a humidity sensor, a pressure and differential pressure sensor, an intelligent control module and the like, and obtains a current characteristic parameter database of the dry etching machine according to the online detection device, wherein the current characteristic parameter database comprises voltage, power supply frequency, response time, power consumption, water flow, vacuum condition, equipment temperature, pressure, load rate, unbalance degree and the like.
And the preventive maintenance suggestion acquisition and pushing module is used for acquiring a plurality of comprehensive preventive values according to the numerical values corresponding to the characteristic parameters and the maintenance threshold values, judging whether the characteristic parameters exceed the maintenance threshold values and the duration time exceeds the preset time, and if so, pushing matched preventive maintenance suggestions and/or alarm processing suggestions. Further, a maintenance threshold value and current characteristic parameters of the dry etching equipment are obtained according to the steps, a correlation analysis method is adopted, and matched preventive maintenance suggestions and/or alarm processing suggestions are/is pushed.
The maintenance threshold value of the dry etching machine is obtained through the maintenance threshold value obtaining module, the characteristic parameter value of the dry etching equipment is obtained through the dynamic data collecting module, and the preventive maintenance suggestion obtaining and pushing module pushes matched preventive maintenance suggestions and/or alarm processing suggestions and pushes the preventative maintenance suggestions and/or the alarm processing suggestions according to the characteristic parameter value and the corresponding maintenance threshold value. The method can carry out preventive maintenance according to the preventive maintenance suggestions, pushes the alarm processing suggestions to carry out preventive alarm on the dry etching equipment, prompts maintenance personnel to carry out preventive maintenance, overcomes the technical problems of low equipment reliability, low efficiency, high maintenance cost and the like caused by passive maintenance in the conventional dry etching equipment maintenance method, realizes prediction or indication of potential faults before actual faults of the dry etching equipment occur, provides better maintenance time for operators or field operation and maintenance personnel, reduces the fault occurrence frequency of the dry etching equipment, changes passive maintenance into active protection, and avoids the occurrence of major equipment faults.
Example 3:
an embodiment of the present invention further provides a storage medium containing a computer-executable program, where the computer-executable program is executed by a computer processor to perform a method for preventive maintenance of a dry etching machine, and the method includes:
analyzing a defect mechanism of a defective dry etching test product by a material structure performance characterization means to obtain defect types and major and minor factors of generating equipment and a process;
establishing a defect prediction model by adopting a statistical method according to an analysis result;
establishing a maintenance threshold value for normal operation of the dry etching machine according to the prediction model;
and pushing a normal/alarm response according to the comparison between the threshold and the obtained current dry etching machine parameter, and matching maintenance content and plan.
Storage media, various types of memory devices or storage devices. The storage medium includes: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbus (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network. The second computer system may provide the program instructions to the computer for execution. A storage medium includes two or more storage media that may reside in different locations (in different computer systems connected by a network). The storage medium may store program instructions (embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium containing the computer-executable instructions provided in the embodiments of the present invention is not limited to the above-mentioned preventive maintenance operation of the dry etching machine, and may also perform the relevant operations in the preventive maintenance method of the dry etching machine provided in any embodiments of the present application.
What has been described above are merely some embodiments of the present invention. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the inventive concept thereof, and these changes and modifications can be made without departing from the spirit and scope of the invention.

Claims (8)

1. The preventive maintenance method of the dry etching machine is characterized by comprising the following steps of:
analyzing the defect mechanism of the defective dry etching test product by means of characterization of material structure and performance to obtain defect types and major and minor factors of generating equipment and process;
according to the analysis result, a defect prediction model is established by adopting a statistical method;
establishing a maintenance threshold value for normal operation of the dry etching machine according to the defect prediction model;
and pushing a normal/alarm response according to the comparison between the maintenance threshold and the acquired characteristic parameters of the current dry etching machine, and matching corresponding maintenance contents and plans.
2. The preventive maintenance method of a dry etcher as defined in claim 1, characterized in that: the characterization means comprises scanning electron microscope characterization, transmission electron microscope characterization and X-ray diffraction.
3. The preventive maintenance method of a dry etcher as defined in claim 1, characterized in that: the defect prediction model is obtained by learning according to data obtained after the tissue and performance of the dry etching test product are represented and the time when the test product has defects or the time when equipment is maintained; or the defect prediction model is obtained by learning according to the dynamic coefficient of the equipment when the dry etching equipment is detected, and the time when the equipment fails or the time when maintenance is carried out.
4. The preventive maintenance method of a dry etcher as defined in claim 1, characterized in that: before the on-line detection device of the dry etching equipment is used for obtaining the equipment characteristic parameters in the current production process of the dry etching machine, the method further comprises the following steps: determining the type and state of a product, and acquiring current temperature and humidity data;
and determining the current equipment parameters corresponding to the dry etching machine according to the product type and state, the current temperature and humidity data.
5. The dry etcher preventive maintenance method according to claim 3, characterized in that: after predicting when a defect will occur in a current dry etch product based on the defect prediction model, the method further comprises: and determining a maintenance scheme of the dry etching machine according to a result of predicting when the current dry etching product has the defects.
6. The preventive maintenance system of the dry etching machine is characterized in that: comprises that
A maintenance threshold value obtaining module for obtaining a maintenance threshold value of the dry etching machine;
the dynamic data acquisition module is used for acquiring numerical values corresponding to a plurality of characteristic parameters of the dry etching machine equipment;
and the preventive maintenance suggestion acquisition and pushing module is interconnected with the maintenance threshold acquisition module and the dynamic data acquisition module and is used for acquiring a plurality of comprehensive preventive values according to the values corresponding to the characteristic parameters and the maintenance threshold and pushing the comprehensive preventive maintenance suggestions and/or the alarm processing suggestions which are matched with the comprehensive preventive maintenance suggestions.
7. The dry etcher preventative maintenance system according to claim 6, wherein: the preventive maintenance suggestion acquisition and pushing module can judge whether the characteristic parameters exceed a maintenance threshold value and the duration time exceeds preset time, and if so, pushes matched preventive maintenance suggestions and/or alarm processing suggestions.
8. The preventive maintenance storage medium of the dry etching machine is characterized in that: the storage medium stores a computer-executable program which, when executed by a computer processor, implements the dry etcher preventive maintenance method as set forth in any one of claims 1 to 5.
CN202010656426.8A 2020-07-09 2020-07-09 Preventive maintenance method and system for dry etching machine and storage medium Pending CN113919515A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116864440A (en) * 2023-09-04 2023-10-10 泓浒(苏州)半导体科技有限公司 Automated handling system, method, apparatus and medium for semiconductor workpieces

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116864440A (en) * 2023-09-04 2023-10-10 泓浒(苏州)半导体科技有限公司 Automated handling system, method, apparatus and medium for semiconductor workpieces
CN116864440B (en) * 2023-09-04 2023-11-14 泓浒(苏州)半导体科技有限公司 Automated handling system, method, apparatus and medium for semiconductor workpieces

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